code: def afx_small():
      """Small transformer model with small batch size for fast step times."""
      hparams = transformer.transformer_tpu()
      hparams.filter_size = 1024
      hparams.num_heads = 4
      hparams.num_hidden_layers = 3
      hparams.batch_size = 512
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import registry
label_desc: Use image node_id 18 for calling a global function with example usage: afx_small() and returns: hparams